Beifen Dai1,#, Zhihao Xu2,#, Hongjue Li3, Bo Wang3, Jinsong Cai1, Xiaomo Liu4,*
Oncologie, Vol.24, No.1, pp. 113-130, 2022, DOI:10.32604/oncologie.2022.020259
Abstract Large-scale genomic studies are important ways to comprehensively decode the human genomics, and provide
valuable insights to human disease causalities and phenotype developments. Genomic studies are in need of high
throughput bioinformatics analyses to harness and integrate such big data. It is in this overarching context that
artificial intelligence (AI) offers enormous potentials to advance genomic studies. However, racial bias is always an
important issue in the data. It is usually due to the accumulation process of the dataset that inevitability involved
diverse subjects with different races. How can race bias affect the outcomes of AI methods? In this work,… More >
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